JP7768966B2 - Medical image processing device, medical image processing method and program - Google Patents

Description

This disclosure relates to a medical image processing device, a medical image processing method, and a program.

Rectal cancer originates in the lumen of the intestine and invades tissue outside the intestine as it progresses. For advanced cancers that require surgery, a total mesorectal excision is performed, in which the mesorectum, the fatty tissue surrounding the rectum, is removed along with the cancer. A total mesorectal excision is abbreviated as TME, which stands for total mesorectal excision.

In surgery where total mesorectal resection is performed, it is necessary to achieve both an adequate margin around the cancer and preservation of important tissues around the mesorectum, such as nerves and blood vessels.

The reason why resection with sufficient margins is required is that a circumferential resection margin (CRM), which is the distance between the cancer and the dissection surface around the rectum, of 1 millimeter or less is considered a strong predictor of local recurrence.

On the other hand, the reason why it is necessary to preserve the surrounding tissue is that thin blood vessels and autonomic nerves run around the mesorectum. If the autonomic nerves are damaged, it may lead to urinary disorders and sexual dysfunction.

Non-Patent Document 1 describes how, in function-preserving rectal cancer surgery, the dissection layer is selected according to the progression of the cancer, preserving the potential for cure. The same document also describes how nerve damage increases or decreases depending on the level of the dissection layer.

Patent Document 1 describes an endoscopic image diagnosis support device that displays a superimposed distance distribution from the bronchial wall to a region of interest outside the bronchus on a volume rendering image. The device described in this document uses three-dimensional images to generate a first projection image and a second projection image in which the region of interest is projected onto the inner wall of the bronchus. The first projection image and the second projection image are highlighted according to the projection distance.

Patent Document 2 describes a blood flow visualization device that uses a computer to automatically analyze vascular lesions. The device described in this document acquires medical images, determines vascular lesions using vascular shapes constructed from the medical images, and calculates the geometric characteristics of the vascular lesions.

Patent Document 3 describes a medical image processing device that measures the thickness of fat regions around coronary arteries. The device described in this document measures the distribution and thickness of fat regions formed around coronary arteries based on volume data, and generates two-dimensional fat thickness distribution data.

Patent Document 4 describes a technology for displaying images used in surgical planning for breast cancer resection surgery. The image processing device described in the document is a device that generates VR images and the like using reconstructed data, identifies the tumor area, calculates the distance on the body surface from a reference point to the tumor area, and displays it on the VR image.

Patent document 5 describes a medical image processing device that divides a subject's organ into multiple regions, corrects the edges and boundaries of the regions, and uses the division results to calculate the region of the organ to be resected.

JP 2016-039874 A International Publication No. 2017/047820 JP 2011-218037 A Japanese Patent Application Laid-Open No. 2009-022369 Japanese Patent Application Laid-Open No. 2020-120828

Yusuke Kinugasa, "Fascia anatomy around the rectum from a surgeon's perspective and the dissection layer for function-preserving rectal cancer surgery" [online], September 8, 2012, Clinical Laboratory Anatomy Research Society, [Retrieved January 25, 2021], Internet (http://www.jrsca.jp/contents/records/contents/PDF/13-PDF/p08.pdf)

However, in order to determine a dissection plane before surgery that will allow for both the removal of a lesion such as cancer with a sufficient margin around the lesion and the preservation of important tissues surrounding the lesion, it is necessary to know the three-dimensional distance between the tissue to be removed and the tissue to be preserved, as well as the three-dimensional distance between the dissection plane and the tissue to be removed.

When determining the three-dimensional distance between tissue to be resected and tissue to be preserved, it is necessary to determine whether the tissue to be measured for three-dimensional distance and the planned dissection plane are selected in the correct position in the medical image. This determination requires evaluation of the two-dimensional cross-sectional image of the three-dimensional medical image. In other words, when examining the dissection plane, it is necessary to clearly display the spatial distance between the objects for which three-dimensional distance is to be measured in the two-dimensional cross-sectional image.

Patent documents 1 to 5 do not describe the display used to evaluate the three-dimensional distance between the dissection surface used to dissect the tissue to be resected and the tissue to be resected, or the three-dimensional distance between the dissection surface and the tissue to be preserved, and do not describe any means for solving this problem.

The present invention has been made in consideration of these circumstances, and aims to provide a medical image processing device, a medical image processing method, and a program that can assist in determining a separation plane based on the three-dimensional distance between the tissue to be resected and the tissue to be preserved.

The medical image processing device of the present disclosure is a medical image processing device that includes a processor and a storage device that stores a program executed by the processor, wherein the processor executes instructions of the program to acquire a three-dimensional medical image, set a separation plane for separating a first region identified in the medical image, measure a first distance that is the closest distance between an arbitrary position on the surface of the first region and the separation plane, measure a second distance that is the closest distance between an arbitrary position on the surface of a second region identified in the medical image and the separation plane, and, when displaying the medical image on a display device, display at least one of first distance information representing the first distance and second distance information representing the second distance.

The medical image processing device disclosed herein allows users to grasp at a glance at least one of the three-dimensional distance between the first region and the separation surface and the three-dimensional distance between the second region and the separation surface, thereby assisting in determining the separation surface.

The first region may identify an area containing tissue to be separated. The second region may identify an area containing tissue to be preserved.

The area to which the separation surface is specified may be a third area that exists between the first and second areas.

In another aspect of the medical image processing device, when a cross-sectional image corresponding to any cross-section in the medical image is displayed on a display device, the processor displays first distance information in an outer edge region on the side of the first region of the separation surface.

This aspect can prevent the visibility of the separation surface from being obstructed. Furthermore, it is possible to grasp at a glance the side of the separation surface to which the first distance applies.

The edge region may be an area that contacts the separation surface or an area that does not contact the separation surface.

In another aspect of the medical image processing device, the processor displays second distance information in an outer edge region on the second region side of the separation surface when displaying a cross-sectional image corresponding to any cross-section in the medical image on a display device.

This configuration can prevent the visibility of the separation surface from being obstructed. Furthermore, it is possible to grasp at a glance the side of the separation surface to which the second distance applies.

In another aspect of the medical image processing device, the processor applies a color map that represents distance using color to display first distance information and second distance information.

According to this aspect, the first distance and the second distance can be grasped at a glance based on the information represented by the colors of the color map.

Examples of color maps include those that use different colors to represent different distances and those that use different densities to represent different distances.

In another aspect of the medical image processing device, the processor displays a volume rendering image corresponding to the medical image on a display device and superimposes at least one of the first distance information and the second distance information on the volume rendering image.

According to this aspect, in the volume rendering image, at least one of the three-dimensional distance between the first region and the separation surface and the three-dimensional distance between the second region and the separation surface can be grasped at a glance, thereby assisting in determining the separation surface.

In another aspect of a medical image processing device, the processor changes the display mode of the first distance information and the display mode of the second distance information depending on the user's viewpoint in the volume rendering image.

According to this aspect, the first distance and second distance from the user's viewpoint can be grasped at a glance.

In another aspect of the medical image processing device, the processor superimposes first distance information onto the first region in the volume rendering image when the first region is located on the user's viewpoint side of the separation surface.

According to this aspect, first distance information can be displayed at a position visible from the user's viewpoint for a first area visible from the user's viewpoint.

In another aspect of the medical image processing device, the processor superimposes second distance information on the separation surface when the second region is located on the opposite side of the separation surface from the user's viewpoint in the volume rendering image.

According to this aspect, second distance information can be displayed at a position visible from the user's viewpoint for a second area that is not visible from the user's viewpoint.

In another aspect of the medical image processing device, the processor superimposes first distance information on the separation surface when the first region is located on the opposite side of the separation surface from the user's viewpoint in the volume rendering image.

According to this aspect, first distance information can be displayed at a position visible from the user's viewpoint for a first area that is not visible from the user's viewpoint.

In another aspect of the medical image processing device, the processor superimposes second distance information onto the second region in the volume rendering image when the second region is located on the user's viewpoint side of the separation surface.

According to this aspect, second distance information can be displayed at a position visible from the user's viewpoint for a second area visible from the user's viewpoint.

The medical image processing method disclosed herein is a medical image processing method in which a computer acquires a three-dimensional medical image, sets a separation plane for separating a first region identified in the medical image, measures a first distance that is the closest distance between an arbitrary position on the surface of the first region and the separation plane, measures a second distance that is the closest distance between an arbitrary position on the surface of a second region identified in the medical image and the separation plane, and, when displaying the medical image on a display device, displays at least one of first distance information representing the first distance and second distance information representing the second distance.

The medical image processing method of the present disclosure can achieve the same effects as the medical image processing device of the present disclosure. The constituent elements of the medical image processing device of other aspects may be applied to the constituent elements of the medical image processing method of other aspects.

The program disclosed herein is a program that enables a computer to perform the following functions: acquire three-dimensional medical images; set a separation plane when separating a first region identified in the medical image; measure a first distance, which is the closest distance between any position on the surface of the first region and the separation plane; measure a second distance, which is the closest distance between any position on the surface of a second region identified in the medical image and the separation plane; and display at least one of first distance information representing the first distance and second distance information representing the second distance when displaying the medical image on a display device.

The program according to the present disclosure can achieve the same effects as the medical image processing device according to the present disclosure. The constituent elements of the medical image processing device according to other aspects may be applied to the constituent elements of the program according to other aspects.

According to the present invention, it is possible to grasp at a glance at least one of the three-dimensional distance between the first region and the separation surface and the three-dimensional distance between the second region and the separation surface, thereby assisting in determining the separation surface.

FIG. 1 shows an example of CRM measurement from an MRI image of a patient with rectal cancer. FIG. 2 is a diagram illustrating stereotactic anterior resection. FIG. 3 is an image diagram showing the orientation of FIG. FIG. 4 shows the resection surface. FIG. 5 is a schematic diagram showing an arbitrary cross section of an MRI image. FIG. 6 is an image diagram showing the orientation of FIG. FIG. 7 is a schematic diagram showing a first example of an ablation line. FIG. 8 is a schematic diagram showing a second example of the ablation line. FIG. 9 is a schematic diagram showing a third example of the ablation line. FIG. 10 is a functional block diagram of a medical image processing apparatus according to an embodiment. FIG. 11 is a block diagram showing an example of the configuration of a medical image processing apparatus according to the embodiment. FIG. 12 is a flowchart showing the procedure of the medical image processing method according to the embodiment. FIG. 13 is an image diagram showing an example of three-dimensional distance information superimposed on a two-dimensional cross section. FIG. 14 is an image diagram showing another example in which three-dimensional distance information is superimposed on a two-dimensional cross section. FIG. 15 is a schematic diagram showing an example of a display screen for the first distance measurement in the distance measurement process. FIG. 16 is a schematic diagram showing an example of a display screen for the second distance measurement in the distance measurement process. FIG. 17 is a schematic diagram showing an example of a display of the first color map. FIG. 18 is a schematic diagram showing an example of a display of the second color map. FIG. 19 is a schematic diagram showing a display example of a color map according to a modified example. FIG. 20 is a schematic diagram showing an example of displaying three-dimensional distance information in a volume rendering image. FIG. 21 is a schematic diagram of FIG. 20 viewed from the tissue to be preserved. FIG. 22 is a schematic diagram of FIG. 20 viewed from the tissue to be excised. FIG. 23 is a schematic diagram showing a display example of three-dimensional distance information applied to each tissue shown in FIG. FIG. 24 is a block diagram showing an example of the configuration of a medical information system including a medical image processing apparatus according to an embodiment.

A preferred embodiment of the present invention will now be described in detail with reference to the accompanying drawings. In this specification, identical components will be designated by the same reference symbols, and duplicate explanations will be omitted where appropriate.

[Example of distance measurement for rectal cancer]
Figure 1 shows an example of CRM measurement from an MRI image of a patient with rectal cancer. In Figure 1, the cancer TU, muscularis propria MP, and mesorectum ME are distinguished by different fill patterns. While Figure 1 shows a two-dimensional image, the MRI image actually obtained is a three-dimensional image. MRI is an abbreviation for Magnetic Resonance Imaging.

Modalities such as MRI and CT scanners take a series of two-dimensional slice images to obtain three-dimensional data showing the three-dimensional shape of an object. The term three-dimensional image can also include the concepts of a collection of two-dimensional slice images taken in succession, as well as a two-dimensional sequence. The term image can also include the meaning of image data. CT is an abbreviation for Computed Tomography.

The arrow in Figure 1 indicates the closest point, the point from the cancer TU to the mesorectum ME. The closest distance, which is the distance from the closest point, is one of the important parameters when determining the stage of cancer. If the closest distance is 1 millimeter or less, the result is considered CRM positive.

[Explanation of stereotactic anterior resection]
Figure 2 is a diagram showing stereotactic anterior resection. Figure 3 is an image showing the orientation of Figure 2. Figure 2 is a view looking into the pelvis from the head side. Figure 3 uses arrows to illustrate the position of the abdominal side in Figure 2.

Figure 2 shows the state of cancer removal using stereotactic anterior resection, which involves removing the fat 2 around the rectum 1 where the cancer has developed and removing the entire cancer. When removing cancer, it is important to completely remove the cancer without damaging the nerves 3.

Figure 4 shows the resection plane. When cancer is removed, it is removed at resection plane 4. If resection plane 4 is located too far inside the rectum 1, the cancer will be exposed. On the other hand, if resection plane 4 is located too far outside the rectum 1, there is a risk of damaging nerves 3.

Figure 5 is a schematic diagram showing an arbitrary cross section of an MRI image. The figure shows a state in which a cancer 5 occurring in the rectum 1 has crossed the boundary 6 between the rectum 1 and fat 2 and reached the fat 2. Figure 6 is an image showing the orientation of Figure 5. Region 7 shown in Figure 6 represents the position of the rectum 1, etc. shown in Figure 5. Note that the rectum 1, fat 2, and cancer 5 shown in Figure 5 correspond to the muscularis propria MP, mesorectum ME, and cancer TU shown in Figure 1, respectively.

Figure 7 is a schematic diagram showing a first example of a resection line. The resection line 4A shown in the figure is set when the cancer 5 is to be resected along with the fat 2 surrounding the rectum 1 where the cancer 5 developed. In other words, the resection line 4A coincides with the outline of the fat 2.

If a resection line 4A is set, there is a risk that nerves 3 may be damaged when cancer 5 is resected. Note that the resection line 4A in the cross-sectional image shown in Figure 7 corresponds to the resection plane 4 shown in Figure 4. The same applies to the resection line 4B shown in Figure 8 and the resection line 4C shown in Figure 9.

Figure 8 is a schematic diagram showing a second example of a resection line. The resection line 4B shown in the figure is set when the cancer 5 is resected while avoiding the nerves 3 and leaving part of the fat 2. In other words, part of the resection line 4B coincides with the outline of the fat 2 and part of the resection line 4B is inside the fat 2. When the resection line 4B is set, there is a risk that the cancer 5 may not be completely removed.

Figure 9 is a schematic diagram showing a third example of a resection line. The resection line 4C shown in this figure is set when the cancer 5 is resected while avoiding the nerve 3 and leaving part of the fat 2. That is, like the resection line 4B shown in Figure 8, part of the resection line 4C coincides with the contour of the fat 2 and part of the resection line 4C is inside the fat 2, while the resection line 4C has sufficient margins from the nerve 3 and the cancer 5.

When the resection plane 4 shown in Figure 4 is set, the medical image processing device described below measures the three-dimensional distance between the cancer 5, which is the tissue to be resected, and the tissue to be preserved, such as nerves 3 and blood vessels, and displays the measurement results on a two-dimensional cross-sectional image to assist in determining the resection plane 4.

From medical images such as MRI images, the three-dimensional distance between the tissue to be preserved and the resection plane 4 shown in Figure 4 is determined, and the three-dimensional distance between the tissue to be resected, such as cancer 5 shown in Figure 9, and the resection plane 4 is determined.

This allows for a resection plane 4 that can be planned before surgery, with a sufficient margin obtained from the tissue to be preserved. If it is determined in advance that a sufficient margin cannot be obtained, it is possible to abandon the preservation of the tissue to be preserved and select a surgical procedure that resects nerves 3, blood vessels, etc.

[Configuration example of a medical image processing device]
Fig. 10 is a functional block diagram of a medical image processing apparatus according to an embodiment. The medical image processing apparatus 20 shown in Fig. 10 is realized using computer hardware and software. Software is synonymous with program. The term medical image used in the following embodiments is synonymous with the term medical image.

The medical image processing device 20 includes an image acquisition unit 222, a lesion area extraction unit 224, a lesion area input reception unit 226, a resection plane extraction unit 228, a resection plane input reception unit 230, a distance measurement unit 232, and a display image generation unit 234. The medical image processing device 20 is also connected to an input device 214 and a display device 216.

The image acquisition unit 222 imports medical images to be processed from an image storage server or the like. In this embodiment, an MRI image taken using an MRI device is used as an example of the medical image to be processed. The medical image to be processed is not limited to an MRI image, and may also be an image taken using another modality such as a CT device.

In this embodiment, imaging diagnosis of cancer occurring in the digestive tract, such as the large intestine, is assumed, and an example of a medical image acquired via the image acquisition unit 222 is a three-dimensional image in which an area including the cancer and its surrounding tissue is captured as a single image. Furthermore, since the medical image processing device 20 measures distance three-dimensionally in millimeter units, it is preferable that the target image be high-resolution three-dimensional data of isotropic voxels.

The lesion area extraction unit 224 performs a lesion area extraction process that applies image recognition to automatically extract a lesion area contained in a medical image acquired via the image acquisition unit 222. In this embodiment, the lesion area is a cancer area. An example of the processing performed by the lesion area extraction unit 224 is to apply machine learning, such as deep learning, to an input medical image and extract a lesion area using a trained model that has been trained to perform an image segmentation task.

An example of a learning model for image segmentation is the convolutional neural network, which can be referred to as CNN, an abbreviation of Convolution Neural Network.

The lesion area extraction unit 224 is not limited to segmentation that classifies lesion areas from other areas, but may also be configured to perform segmentation that classifies each of multiple areas surrounding a lesion area. For example, the lesion area extraction unit 224 may extract lesion areas and conserving areas using a trained model that receives input of a three-dimensional image, classifies cancer areas as lesion areas, and other surrounding organs as conserving areas, and outputs images of the segmentation results with mask patterns applied to each area.

The lesion area input receiving unit 226 receives input of information about lesion areas automatically extracted using the lesion area extraction unit 224. The lesion area input receiving unit 226 also receives input of information about lesion areas specified by the user using the input device 214. The doctor, who is the user, can use the input device 214 to freely specify an area different from the lesion area automatically extracted by the lesion area extraction unit 224. For example, the doctor can use the input device 214 to specify an area that is recognized as cancer or is suspected of being cancerous, among areas that were not extracted as cancer during automatic extraction.

Note that the lesion area described in the embodiments is an example of a first area identified in a medical image. The preserved area described in the embodiments is an example of a second area identified in a medical image.

The resection plane extraction unit 228 performs processing to extract candidates for the resection plane 4, which serves as a reference when resecting the lesion area from the medical image, based on the information on the lesion area obtained via the lesion area input reception unit 226. Since the extent of cancer spread varies depending on the stage of the cancer, an appropriate candidate for the resection plane 4 is automatically extracted depending on the stage of the cancer. Note that the candidate for the resection plane 4 may be a curved surface of any shape.

The resection plane input receiving unit 230 receives input of information on candidate resection planes 4 automatically extracted using the resection plane extraction unit 228. The resection plane input receiving unit 230 also receives input of information on resection planes specified by the user using the input device 214. The doctor, who is the user, can use the input device 214 to specify a resection plane 4 that is different from the candidate resection planes 4 automatically extracted using the resection plane extraction unit 228.

A resection plane 4 that differs from the automatically extracted resection plane 4 candidate may be a surface in which a portion of the automatically extracted resection plane 4 candidate has been modified. The resection plane 4 is determined based on information input via the resection plane input receiving unit 230. Note that, like the resection plane 4 candidate, the resection plane 4 may be a curved surface of any shape.

For example, when considering the resection plane before surgery, a doctor can specify not only real anatomical boundary surfaces, but also virtual surfaces within layers as candidates for resection plane 4. Note that the resection plane 4 described in the embodiment is an example of a separation plane used when separating a first region identified in a medical image.

The distance measurement unit 232 measures the three-dimensional distance from the cancer 5 to the resection plane 4 and the three-dimensional distance from the tissue to be preserved to the resection plane. The tissue to be preserved is tissue located on the opposite side of the resection plane 4 from the cancer 5. An example of tissue to be preserved is the nerve 3 shown in Figure 9.

When a user specifies a lesion area, one possible configuration is to measure the distance from the specified area to the resection surface 4. It is also possible to group the entire lesion area, including the automatically extracted lesion area and the lesion area specified by the user, and treat the entire area as a single lesion area, and measure the distance between the lesion area and the resection surface 4. Preferably, the system is configured to allow selection of these measurement modes.

The following method can be used to measure distance: For each of multiple points on the contour of the lesion area to be measured, the distance to the closest point on the resection plane 4 is calculated, and the shortest distance between the two points among the multiple calculated distances is obtained as the nearest distance between the resection plane 4 and the lesion area.

Similar to measuring the distance between the resection plane 4 and the lesion area, the distance between the resection plane 4 and the preservation area is measured, and the nearest distance between the resection plane 4 and the preservation area is obtained. Note that the multiple points on the contour of the lesion area described in the embodiment are an example of arbitrary positions on the surface of the first area. The multiple points on the contour of the preservation area described in the embodiment are an example of arbitrary positions on the surface of the second area.

The display image generation unit 234 generates a display image to be displayed on the display device 216. The display image generation unit 234 generates a two-dimensional image for display from a three-dimensional image. Examples of two-dimensional images for display include cross-sectional images and volume rendering images.

The display image generation unit 234 generates at least one cross-sectional image from among an axial cross-sectional image, a coronal cross-sectional image, and a sagittal cross-sectional image. The display image generation unit 234 may also generate all three types of cross-sectional images.

The display image generation unit 234 generates an image representing the resection plane 4 determined using the resection plane extraction unit 228. The image representing the resection plane 4 is superimposed on a cross-sectional image, etc. An example of an image representing the resection plane 4 superimposed on a cross-sectional image is the resection line 4C shown in Figure 9.

The display image generation unit 234 generates first distance information representing the distance between the resection plane 4 and the lesion area measured using the distance measurement unit 232. The display image generation unit 234 also generates second distance information representing the distance between the resection plane 4 and the preservation area measured using the distance measurement unit 232.

The display image generation unit 234 may apply graphic elements such as a color map and a color bar as the first distance information and the second distance information. The first distance information and the second distance information generated using the display image generation unit 234 are superimposed on the cross-sectional image.

Figure 11 is a block diagram showing an example configuration of a medical image processing device according to an embodiment. The medical image processing device 20 includes a processor 202, a non-transitory tangible computer-readable medium 204, a communication interface 206, and an input/output interface 208.

The medical image processing device 20 may take the form of a server, a personal computer, a workstation, a tablet terminal, etc.

The processor 202 includes a CPU (Central Processing Unit). The processor 202 may also include a GPU (Graphics Processing Unit). The processor 202 is connected to a computer-readable medium 204, a communication interface 206, and an input/output interface 208 via a bus 210. An input device 214 and a display device 216 are connected to the bus 210 via the input/output interface 208.

The input device 214 may be a keyboard, a mouse, a multi-touch panel, other pointing devices, a voice input device, etc. The input device 214 may be any combination of multiple devices.

The display device 216 may be a liquid crystal display, an organic EL display, a projector, or the like. The display device 216 may be any combination of multiple devices. Note that the "EL" in organic EL display is an abbreviation for Electro-Luminescence.

The computer-readable medium 204 includes memory, which is a primary storage device, and storage, which is a secondary storage device. The computer-readable medium 204 may be a semiconductor memory, a hard disk drive, a solid-state drive, or the like. The computer-readable medium 204 may be any combination of multiple devices.

Note that a hard disk device may be referred to as an HDD, which is an abbreviation for Hard Disk Drive in English. A solid state drive device may be referred to as an SSD, which is an abbreviation for Solid State Drive in English.

The medical image processing apparatus 20 is connected to a communication line via a communication interface 206, and is communicably connected to devices such as a DICOM server 40 and a viewer terminal 46 connected to an intra-medical institution network. The communication line is not shown. DICOM is an abbreviation for Digital Imaging and Communications in Medicine.

The computer-readable medium 204 stores multiple programs, including a medical image processing program 220 and a display control program 260. The computer-readable medium 204 stores various data, various parameters, etc.

The processor 202 executes the instructions of the medical image processing program 220 and functions as an image acquisition unit 222, a lesion area extraction unit 224, a lesion area input reception unit 226, a resection plane extraction unit 228, a resection plane input reception unit 230, a distance measurement unit 232, and a display image generation unit 234.

The display control program 260 generates the display signals necessary for display output to the display device 216 and controls the display of the display device 216.

[Procedure of medical image processing method]
12 is a flowchart showing the procedure of the medical image processing method according to the embodiment. In an image acquisition step S12, the processor 202 acquires a medical image to be processed from the DICOM server 40 or the like.

In the lesion area extraction step S14, the processor 202 automatically extracts a lesion area from the input medical image as tissue to be resected. Also, in the lesion area extraction step S14, the processor 202 extracts a preservation area from the input medical image as tissue to be preserved.

Generally, when a lesion is resected, the membrane surrounding the fat surrounding the organ in which the lesion is located is also removed. In the lesion area extraction step S14, the fat area on which the resection plane can be set and the membrane surrounding the fat area are extracted.

Examples of areas to be preserved include blood vessels, nerves, pelvis, bones, muscles, cancer, lymph nodes, ureters, and organs. The organ may be another organ in the organ containing the lesion, or an area other than the lesion in the organ containing the lesion.

The processor 202 may use the trained model to segment the medical image and extract lesion areas and sparing areas. The processor 202 may determine the automatically extracted lesion areas and sparing areas as targets for measuring distance.

In the resection plane determination step S16, the processor 202 automatically extracts a candidate for the resection plane 4 based on the identified lesion area. The processor 202 may extract a membrane enveloping a fatty area as a candidate for the resection plane 4. If the processor 202 does not receive user input indicating a modification of the candidate for the resection plane 4, it may determine the automatically extracted candidate for the resection plane 4 as the resection plane 4.

When automatically extracting candidates for resection plane 4, processor 202 may extract candidates for resection plane 4 taking into account a margin previously specified by the user. For example, if a margin of 2 millimeters is specified, the outer periphery of the mesorectum may be extracted as a candidate for resection plane 4 in areas where the CRM is 2 millimeters or more, and a plane beyond the mesorectum, 2 millimeters from the outer periphery of the cancer, may be extracted as a candidate for resection plane 4 in areas where the CRM is less than 2 millimeters.

In the distance measurement step S18, the processor 202 measures a first distance, which is the closest distance between the resection surface 4 and the lesion area. Also, in the distance measurement step S18, the processor 202 measures a second distance, which is the closest distance between the resection surface 4 and the preservation area.

In the display image generation process S20, the processor 202 generates a display image in which first distance information representing the first distance and second distance information representing the second distance are superimposed on any cross-sectional image.

In the display step S22, the processor 202 displays the generated display image on the display device 216. This allows the user to grasp at a glance the distance between the resection plane 4 and the lesion area in the cross-sectional image, and the distance between the resection plane 4 and the preservation area.

The display image generation process S20 may generate a display image representing the processing results of the lesion area extraction process S14 and the resection plane determination process S16. The display process S22 may display the display image representing the processing results of the lesion area extraction process S14 and the resection plane determination process S16 on the display device 216.

In the user input reception step S24, the processor 202 receives various instructions from the user. The user can enter various inputs, such as specifying the lesion area and the resection plane 4, from the input device 214.

In the lesion area designation input determination step S26, the processor 202 determines whether or not there has been input from the input device 214 specifying a lesion area and an input specifying a conservation area. If the user performs an operation such as specifying a location in the medical image that is suspected to be a lesion, and information specifying a lesion area and information specifying a conservation area is input, the determination is YES. If the determination is YES, the processor 202 proceeds to the lesion area extraction step S14 and determines the specified lesion area and conservation area as targets for distance measurement.

On the other hand, if the processor 202 determines in the lesion area designation input determination step S26 that there is no input specifying the lesion area, the result is a NO determination. If the result is a NO determination, the processor 202 proceeds to the resection plane designation step S28.

In the resection plane designation step S28, the processor 202 determines whether or not there has been an input specifying resection plane 4 from the input device 214. If the user performs an operation to designate resection plane 4 and information specifying resection plane 4 has been input, the result is a YES determination. If the result is a YES determination, the processor 202 proceeds to the resection plane determination step S16 and determines the designated resection plane 4 as the reference for distance measurement.

On the other hand, if the processor 202 determines in the resection plane designation step S28 that no information specifying the resection plane 4 has been input, the result is a NO judgment. If the result is a NO judgment, the processor 202 proceeds to the termination determination step S30.

When editing the resection plane 4 based on user input, it is preferable to implement an editing restriction that does not accept edits that do not have a sufficient margin for the closest distance between the resection plane 4 and the lesion area.

For example, if an edit is instructed that would cause the closest distance between the resection plane 4 and the lesion area to be less than a distance previously specified by the user, the edit may be invalidated. If the edit is invalidated, an error message may be displayed on the display device 216.

In the termination determination step S30, the processor 202 determines whether to terminate the display of the medical image. The condition for terminating the display may be a termination instruction input by the user or a termination command based on the program. If the termination condition is not met and the determination result of the termination determination step S30 is NO, the processor 202 proceeds to the display step S22 and continues the display.

On the other hand, in the termination determination step S30, if the termination condition is met, such as when the user closes the display window, a YES determination is made. If the YES determination is made, the processor 202 terminates the display and ends the flowchart of Figure 12.

In addition, the processing functions of the medical image processing device 20 can also be realized by sharing the processing using multiple computers.

[Display example of first distance information and second distance information]
13 is an image diagram showing an example of superimposing three-dimensional distance information on a two-dimensional cross section. This figure shows a two-dimensional cross-sectional image of an arbitrary axial cross section. In the cross-sectional image 300 shown in this figure, a resection line 301 has been adjusted based on the specification of the user (physician) in the region where the cancer 5 is close to the nerve 3.

A first color map 302 showing the distribution of nearest neighbor distances between the resection line 301 and the cancer 5 is displayed in the outer edge region of the resection line 301 on the cancer 5 side. A second color map 304 showing the distribution of nearest neighbor distances between the resection line 301 and the nerve 3 is displayed in the outer edge region of the resection line 301 on the nerve 3 side.

The outer edge region of the ablation line 301 may be an area that is in contact with the ablation line 301. The outer edge region of the ablation line 301 may be an area that is not in contact with the ablation line 301.

The distance from the ablation line 301 to the first color map 302 is preferably equal to or less than the width of the ablation line 301 or the width of the first color map 302. Similarly, the distance from the ablation line 301 to the second color map 304 is preferably equal to or less than the distance of the ablation line 301 or the width of the second color map 304.

The first color map 302 shows the distribution of the nearest neighbor distance between the resection line 301 and the cancer 5 for each position on the resection line 301. The resection line 301 shown in Figure 13 has a narrower line width the shorter the nearest neighbor distance between the resection line 301 and the cancer 5, and a wider line width the longer the nearest neighbor distance between the resection line 301 and the cancer 5. For convenience of illustration, Figure 13 shows the first color map 302 with a uniform line width. The same is true for the second color map 304.

In the first color map 302, an arbitrary color is applied to areas where the nearest distance between the resection line 301 and the cancer 5 is equal to or less than a specified threshold. The first color map 302 may be adapted to display the numerical value of the nearest distance between the resection line 301 and the cancer 5 when user input such as a mouse over or click is acquired. For convenience of illustration, the first color map 302 shown in Figure 13 is not colored.

The same applies to the second color map 304. The first color map 302 and the second color map 304 can be distinguished from each other by using different colors.

Figure 13 shows a cross-sectional image 300 in which a first color map 302 and a second color map 304 are displayed for a portion of the resection line 301. The first color map 302 and the second color map 304 may be displayed for multiple regions of the resection line 301.

That is, the first color map 302 may be hidden in areas where the nearest neighbor distance between the resection line 301 and the cancer 5 exceeds a specified distance. Similarly, the second color map 304 may be hidden in areas where the nearest neighbor distance between the resection line 301 and the nerve 3 exceeds a specified distance.

For example, at least one of the first color map 302 and the second color map 304 may be displayed for the area of the resection line 301 determined according to the specification of the user/doctor.

Figure 14 is an image diagram showing another example of superimposing three-dimensional distance information on a two-dimensional cross section. The cross-sectional image 310 shown in the figure displays a first color map 312 and a second color map 314 for the entire area of the resection line 301.

The first color map 312 shown in FIG. 14 may have a configuration similar to that of the first color map 302 shown in FIG. 13. Similarly, the second color map 314 shown in FIG. 14 may have a configuration similar to that of the second color map 304 shown in FIG. 13.

[Example of user interface]
Next, a specific example of the user interface will be described. The display device 216 shown in Fig. 10 displays cross-sectional images showing the results of the processing performed in each step shown in Fig. 12.

In the lesion area extraction step S14, the display device 216 displays the segmentation results of the lesion area and the conservation area. For example, the display device 216 displays the cross-sectional image shown in Figure 5 as the processing result of the lesion area extraction step S14.

In the resection plane determination step S16, the display device 216 displays the resection line 4A shown in Figure 7, the resection line 4B shown in Figure 8, and the resection line 4C shown in Figure 9 as candidate resection lines superimposed on the cross-sectional image shown in Figure 5. The display device 216 may display the resection line 4C shown in Figure 9 as the determined resection line.

Figure 15 is a schematic diagram showing an example of the display screen for the first distance measurement in the distance measurement process. The display window 320 shown in the figure is an example of the display mode in the distance measurement process S18. The display window 320 can also be used to display the processing results in the lesion area extraction process S14 and the resection plane determination process S16.

The display window 320 shown in Figure 15 includes a main display area 322, a first sub-display area 324A, a second sub-display area 324B, and a third sub-display area 324C. The main display area 322 is positioned in the center of the display window 320.

The first sub-display area 324A, the second sub-display area 324B, and the third sub-display area 324C are arranged to the left of the main display area 322 in the same figure. The first sub-display area 324A, the second sub-display area 324B, and the third sub-display area 324C are arranged in a vertical line in the same figure.

15 , an axial cross-sectional image is displayed in the main display area 322, and an arrow line 332 indicating the distance between an arbitrary position of the resection line 301 and the cancer 5 is superimposed on the axial cross-sectional image. A numerical value may be displayed instead of the arrow line 332.

Figure 15 illustrates the cross-sectional image 310 shown in Figure 14 as an axial cross-sectional image. The same applies to the axial cross-sectional images in Figures 16 to 19.

Figure 16 is a schematic diagram showing an example of a display screen for the second distance measurement in the distance measurement process. Figure 16 shows an example in which an arrow line 334 indicating the distance between the resection line 301 and the nerve 3 is displayed. The arrow line 332 shown in Figure 15 and the arrow line 334 shown in Figure 16 may be displayed on the same display screen.

The arrow line 332 shown in Figure 15 and the arrow line 334 shown in Figure 16 may be displayed for all distance measurement positions, or may be displayed for only a portion of distance measurement positions, such as representative measurement positions.

Figure 17 is a schematic diagram showing an example of the display of the first color map. This figure shows the first color map 312 shown in Figure 14. The first color map 312 shown in Figure 17 uses both line width and color to display the nearest neighbor distance. That is, depending on the nearest neighbor distance between the resection line 301 and the cancer 5, the first color map 312 becomes darker as the nearest neighbor distance between the resection line 301 and the cancer 5 becomes relatively shorter, and becomes lighter as the nearest neighbor distance between the resection line 301 and the cancer 5 becomes relatively longer.

For example, the first color region 312A in the first color map 312 is darker than the second color region 312B and the third color region 312C, and the resection line 301 and the cancer 5 are closer in the first color region 312A than in the second color region 312B and the third color region 312C.

The first color map 312 shown in Figure 17 shows a gradual change in the nearest neighbor distance between the resection line 301 and the cancer 5, but a first color map that continuously shows a change in the nearest neighbor distance between the resection line 301 and the cancer 5 may also be applied.

17 applies density to represent the difference in the nearest neighbor distance between the resection line 301 and the cancer 5, but color, saturation of the same color, or brightness of the same color may also be applied to represent the difference in the nearest neighbor distance between the resection line 301 and the cancer 5. The same applies to the second color map 314 shown in FIG.

Figure 18 is a schematic diagram showing an example of the display of a second color map. This figure shows the second color map 314 shown in Figure 14. The second color map 314 is configured in the same manner as the first color map 312 shown in Figure 17. A detailed description of the second color map 314 will be omitted here.

Figure 19 is a schematic diagram showing an example of a color map display related to a modified example. Figure 19 shows an example in which a first color map 312 and a second color map 314 are superimposed on a cross-sectional image 310.

Either the first color map 312 or the second color map 314 may be selectively superimposed on the cross-sectional image 310. A mode in which only the first color map 312 is displayed, a mode in which only the second color map 314 is displayed, or a mode in which both the first color map 312 and the second color map 314 are displayed may be selectively switched in response to user input.

Figures 15 to 19 illustrate examples of displaying the first color map 312 and second color map 314 shown in Figure 14, but the first color map 302 and second color map 304 shown in Figure 13 may also be displayed.

[Example of application to volume rendering images]
Similar to the cross-sectional image 300 shown in Figure 13, the first distance information corresponding to the first color map 302 and the second distance information corresponding to the second color map 304 shown in Figure 13 can also be superimposed on the volume rendering image.

The display image generation unit 234 shown in FIG. 10 may generate a volume rendering image from a group of multiple cross-sectional images. The display image generation unit 234 may generate multiple volume rendering images with different user viewpoints. The display image generation unit 234 may generate a color map corresponding to the volume rendering image.

Figure 20 is a schematic diagram showing an example of the display of three-dimensional distance information in a volume rendering image. Figure 20 shows a volume rendering image 400 viewed from above, showing the resection plane 402, the tumor 404, which is the tissue to be resected, and the blood vessels 410, which are the tissue to be preserved.

Note that arrow 420 indicates the direction of viewpoint when viewing resection surface 402 from the blood vessel 410 side. Also, arrow 422 indicates the direction of viewpoint when viewing resection surface 402 from the tumor 404 side.

A first color map 406A is superimposed on the tumor 404 at a position opposite the resection plane 402. The first color map 406A represents information on the nearest neighbor distance between the resection plane 402 and the tumor 404. The first color map 406A is applied to the volume rendering image when viewed from the tumor 404 side of the resection plane 402.

Figure 21 is a schematic diagram of Figure 20 viewed from the side of the tissue to be preserved. In the volume rendering image 424 shown in Figure 21, the first color map 406B is displayed on the surface of the resection plane 402 on the side of the blood vessel 410.

The position of the first color map 406B on the resection surface 402 corresponds to the position of the tumor 404 when the tumor 404 is projected onto the resection surface 402. The position of the tumor 404 may be the position of the center of gravity of the tumor 404. The shape of the first color map 406B corresponds to the shape of the tumor 404 when the tumor 404 is projected onto the resection surface 402.

The first color map 406B is located on the opposite side of the resection surface 402 from the blood vessel 410 and indicates the nearest distance between the tumor 404, which is not visible in the volume rendering image 424, and the resection surface 402. The first color map 406B is applied with colors that can be distinguished from the resection surface 402 and the blood vessel 410.

The first color map 406B is applied with a density corresponding to the nearest neighbor distance between the tumor 404 and the resection surface 402. For example, if the nearest neighbor distance between the tumor 404 and the resection surface 402 is relatively short, the density of the first color map 406B is made relatively dark.

The volume rendering image 424 may be superimposed with a second color map representing the nearest neighbor distance between the resection plane 402 and the blood vessel 410. The second color map is not shown in Figure 21.

Figure 22 is a schematic diagram of Figure 20 viewed from the side of the tissue to be resected. The volume rendering image 430 shown in Figure 22 has the second color map 412B superimposed on the surface of the resection plane 402 on the side of the tumor 404.

The second color map 412B is located on the opposite side of the resection surface 402 from the tumor 404 and indicates the nearest distance between the resection surface 402 and the blood vessel 410 that is not visible in the volume rendering image 430. The first color map 406B is applied with colors that can be distinguished from the resection surface 402 and the tumor 404.

The volume rendering image 430 may be superimposed with a first color map representing the nearest neighbor distance between the resection plane 402 and the tumor 404. The first color map is not shown in Figure 22.

The position of the second color map 412B on the resection surface 402, the shape of the second color map 412B, and the display mode of the nearest neighbor distance between the resection surface 402 and the blood vessel 410 are the same as those of the first color map 406B shown in Figure 21.

Figure 23 is a schematic diagram showing an example of the display of three-dimensional distance information applied to each tissue shown in Figure 20. The volume rendering image 440 shown in Figure 23 displays a first color map 406A representing the nearest neighbor distance between the resection surface 402 and the tumor 404 and a second color map 412A representing the nearest neighbor distance between the resection surface 402 and the blood vessel 410 superimposed on each other.

The first color map 406A is the tissue on the tumor 404 side of the resection surface 402 and is displayed on the tissue between the resection surface 402 and the tumor 404. The second color map 412A is the tissue on the blood vessel 410 side of the resection surface 402 and is displayed on the tissue between the resection surface 402 and the blood vessel 410.

In other words, in the volume rendering image viewed from the viewpoint of the tissue to be preserved, if the tissue to be resected is on the opposite side of the resection plane and cannot be seen, a first color map is displayed on the resection plane. Also, a second color map is displayed on the tissue between the resection plane and the tissue to be preserved.

Similarly, in a volume rendering image viewed from the viewpoint of the tissue to be resected, if the tissue to be preserved is on the opposite side of the resection plane and the tissue to be preserved is not visible, a second color map is displayed on the resection plane, and a first color map is displayed on the tissue between the resection plane and the tissue to be preserved.

[Example of application to medical information systems]
24 is a block diagram showing an example of the configuration of a medical information system including a medical image processing apparatus according to an embodiment. The medical information system 100 is a computer network established in a medical institution such as a hospital.

The medical information system 100 comprises a modality 30 for capturing medical images, a DICOM server 40, a medical image processing device 20, an electronic medical record system 44, and a viewer terminal 46. The components of the medical information system 100 are connected via a communication line 48. The communication line 48 may be an in-house communication line within a medical institution. In addition, part of the communication line 48 may be a wide-area communication line.

Examples of modalities 30 include CT devices 31, MRI devices 32, ultrasound diagnostic devices 33, PET devices 34, X-ray diagnostic devices 35, X-ray fluoroscopy diagnostic devices 36, and endoscopic devices 37. There may be various combinations of types of modalities 30 connected to the communication line 48 depending on the medical institution. PET is an abbreviation for Positron Emission Tomography.

The DICOM server 40 is a server that operates according to DICOM specifications. The DICOM server 40 is a computer that stores and manages various data, including images captured using the modality 30, and is equipped with a large-capacity external storage device and a database management program.

The DICOM server 40 communicates with other devices via the communication line 48, sending and receiving various data including image data. The DICOM server 40 receives various data including image data generated using the modality 30 via the communication line 48, and stores and manages it on a recording medium such as a large-capacity external storage device. The storage format of the image data and communication between devices via the communication line 48 are based on the DICOM protocol.

The medical image processing apparatus 20 can acquire data from the DICOM server 40 or the like via a communication line 48. The medical image processing apparatus 20 can also send processing results to the DICOM server 40 and the viewer terminal 46. The processing function of the medical image processing apparatus 20 may be installed in the DICOM server 40 or the viewer terminal 46.

Various information, including various data stored in the database of the DICOM server 40 and processing results generated by the medical image processing device 20, can be displayed on the viewer terminal 46.

The viewer terminal 46 is a terminal for viewing images, called a PACS viewer or DICOM viewer. Multiple viewer terminals 46 can be connected to the communication line 48. The form of the viewer terminal 46 is not particularly limited, and may be a personal computer, a workstation, a tablet terminal, or the like. The viewer terminal 46 may be configured so that the input device can be used to specify the lesion area and the measurement reference plane, etc.

[Example of application to a program that runs a computer]
A program that causes a computer to realize the processing functions of the medical image processing device 20 can be stored in a computer-readable medium, such as an optical disk, a magnetic disk, a semiconductor memory, or other tangible, non-transitory information storage medium, and the program can be provided through the information storage medium.

In addition, instead of providing the program by storing it on a tangible, non-transitory computer-readable medium, it is also possible to provide the program signal as a download service using telecommunications lines such as the Internet.

The hardware structure of the processing units that execute various processes in the medical image processing apparatus 20, such as the image acquisition unit 222, lesion area extraction unit 224, lesion area input reception unit 226, resection plane extraction unit 228, resection plane input reception unit 230, distance measurement unit 232, and display image generation unit 234, is, for example, the various processors shown below. Note that the processing units may include those referred to as processing units. The processor may also be referred to as the English term "processor."

Various types of processors include CPUs, which are general-purpose processors that execute programs and function as various processing units, GPUs, which are processors specialized for image processing, programmable logic devices such as FPGAs, which are processors whose circuit configuration can be changed after manufacture, and dedicated electrical circuits such as ASICs, which are processors with circuit configurations designed specifically to perform specific processes.

Note that FPGA is an abbreviation for Field Programmable Gate Array. ASIC is an abbreviation for Application Specific Integrated Circuit. Programmable logic devices can be referred to as PLDs, which are the abbreviations for Programmable Logic Devices in English.

A single processing unit may be composed of one of these various processors, or two or more processors of the same or different types. For example, a single processing unit may be composed of multiple FPGAs, or a combination of a CPU and an FPGA, or a combination of a CPU and a GPU. Multiple processing units may also be composed of a single processor. An example of multiple processing units composed of a single processor is, first, a configuration in which a single processor is composed of a combination of one or more CPUs and software, as typified by computers such as client and server, and this processor functions as multiple processing units. A second configuration is one in which a processor is used to realize the functions of an entire system including multiple processing units on a single IC chip, as typified by systems on chips. In this way, the various processing units are composed of one or more of the various processors listed above as a hardware structure.

Note that a system on chip can be referred to as SoC, an abbreviation of System On Chip in English. IC is an abbreviation for Integrated Circuit.

More specifically, the hardware structure of these various processors is an electrical circuit that combines circuit elements such as semiconductor devices. Incidentally, electrical circuits can also be referred to using the English term "circuitry."

[Operational effects of the medical image processing apparatus according to the embodiment]
The medical image processing apparatus 20 according to the embodiment provides the following advantageous effects.

[1]
When resecting tissue to be resected, such as a lesion area, a resection plane is specified between the tissue to be resected and the tissue to be preserved, and the nearest distance between the resection plane and the tissue to be resected and the nearest distance between the resection plane and the tissue to be preserved are measured. Based on the measurement results, first distance information representing information on the nearest distance between the resection plane and the tissue to be resected and second distance information representing information on the nearest distance between the resection plane and the tissue to be preserved are superimposed and displayed on the two-dimensional cross-sectional image. This can assist in determining the resection plane when resecting the tissue to be resected.

[2]
The first distance information displayed on the cross-sectional image is displayed on the side of the tissue to be resected of the resection plane, in the outer edge region of the resection plane, thereby preventing a decrease in visibility of the resection plane and allowing the visibility of the first distance information and the side to which the first distance is applied to be grasped at a glance.

[3]
The second distance information displayed on the cross-sectional image is displayed on the side of the tissue to be preserved on the resection plane, in the outer edge region of the resection plane, thereby preventing a decrease in visibility of the resection plane and allowing the user to grasp at a glance the visibility of the second distance information and the side to which the second distance is applied.

[4]
The first distance information displayed on the volume rendering image is displayed on the side of the resection plane that is the tissue to be preserved, so that the first distance information corresponding to the tissue to be resected that is hidden by the resection plane can be visually recognized.

[5]
The second distance information displayed on the volume rendering image is displayed on the side of the resection plane that is the tissue to be resected, so that the second distance information corresponding to the tissue to be preserved that is hidden by the resection plane can be visually recognized.

[6]
A color map is applied to the first distance information and the second distance information, so that a user who sees the first distance information and the second distance information can understand the content of the first distance information at a glance.

The embodiments of the present invention described above may have their constituent elements modified, added, or deleted as appropriate, without departing from the spirit of the present invention. The present invention is not limited to the embodiments described above, and many modifications may be made by those with ordinary skill in the art within the technical spirit of the present invention. Furthermore, the embodiments, modifications, and applications may be implemented in combination as appropriate.

1 Rectum 2 Fat 3 Nerve 4 Resection plane 4A Resection line 4B Resection line 4C Resection line 5 Cancer 6 Boundary 7 Region 20 Medical image processing device 30 Modality 31 CT device 32 MRI device 33 Ultrasound diagnostic device 34 PET device 35 X-ray diagnostic device 36 X-ray fluoroscopy diagnostic device 37 Endoscopy device 40 DICOM server 44 Electronic medical record system 46 Viewer terminal 48 Communication line 100 Medical information system 202 Processor 204 Computer readable medium 206 Communication interface 208 Input/output interface 210 Bus 214 Input device 216 Display device 220 Medical image processing program 222 Image acquisition unit 224 Lesion area extraction unit 226 Lesion area input reception unit 228 Resection plane extraction unit 230 Resection plane input reception unit 232 Distance measurement unit 234 Display image generation unit 260 Display control program 300 Cross-sectional image 302 First color map 304 Second color map 310 Cross-sectional image 312 First color map 312A First color region 312B Second color region 312C Third color region 314 Second color map 320 Display window 322 Main display area 324A First sub-display area 324B Second sub-display area 324C Third sub-display area 332 Arrow line 334 Arrow line 402 Resection plane 404 Tumor 406A First color map 406B First color map 410 Blood vessel 412A Second color map 412B Second color map 420 Arrow line 422 Arrow line 424 Volume rendering image 430 Volume rendering image 440 Volume rendering image ME Mesorectum MP Muscle layer proper TU Cancer S12 to S30 Steps of medical image processing method

Claims (13)

a processor;
a storage device in which a program executed by the processor is stored,
The processor executes the instructions of the program,
Acquire three-dimensional medical images,
setting a separation plane for separating a first region identified in the medical image;
measuring a first distance, which is the closest distance between an arbitrary position on the surface of the first region and the separation surface;
measuring a second distance, which is the nearest distance between an arbitrary position on the surface of the second region identified in the medical image and the separation surface;
A medical image processing apparatus that displays at least one of first distance information representing the first distance and second distance information representing the second distance when displaying the medical image on a display device. The processor:
The medical image processing device according to claim 1 , wherein when a cross-sectional image corresponding to an arbitrary cross-section in the medical image is displayed on the display device, the first distance information is displayed in an outer edge region on the side of the first region of the separation surface. The processor:
3. A medical image processing device as described in claim 1 or 2, wherein when a cross-sectional image corresponding to any cross-section in the medical image is displayed on the display device, the second distance information is displayed in an outer edge area on the side of the second area of the separation surface. The processor:
The medical image processing apparatus according to claim 1 , wherein the first distance information and the second distance information are displayed using a color map that represents distance using color. The processor:
displaying a volume rendering image corresponding to the medical image on the display device;
The medical image processing apparatus according to claim 1 , wherein at least one of the first distance information and the second distance information is displayed superimposed on the volume rendering image. The processor:
The medical image processing apparatus according to claim 5 , wherein a display mode of the first distance information and a display mode of the second distance information are changed depending on a user's viewpoint on the volume rendering image. The processor:
The medical image processing device according to claim 6 , wherein, when the first region is present on the side of the separation plane on which the user's viewpoint is located in the volume rendering image, the first distance information is superimposed on the first region. The processor:
The medical image processing device according to claim 6 or 7, wherein when the second region exists on the opposite side of the separation surface from the user's viewpoint in the volume rendering image, the second distance information is superimposed and displayed on the separation surface. The processor:
The medical image processing device according to claim 6 , wherein when the first region exists on the opposite side of the separation plane from the user's viewpoint in the volume rendering image, the first distance information is superimposed and displayed on the separation plane. The processor:
10. A medical image processing device according to claim 6 or 9, wherein, when the second region is present on the side of the separation surface on which the user is viewing the volume rendering image, the second distance information is superimposed on the second region. The computer
Acquire three-dimensional medical images,
setting a separation plane for separating a first region identified in the medical image;
measuring a first distance, which is the closest distance between an arbitrary position on the surface of the first region and the separation surface;
measuring a second distance, which is the nearest distance between an arbitrary position on the surface of the second region identified in the medical image and the separation surface;
A medical image processing method in which, when the medical image is displayed on a display device, at least one of first distance information representing the first distance and second distance information representing the second distance is displayed. On the computer,
The ability to capture three-dimensional medical images;
a function of setting a separation plane when separating a first region identified in the medical image;
a function of measuring a first distance, which is the closest distance between an arbitrary position on the surface of the first region and the separation surface;
A program that realizes a function of measuring a second distance, which is the closest distance between an arbitrary position on the surface of a second region identified in the medical image and the separation surface, and a function of displaying at least one of first distance information representing the first distance and second distance information representing the second distance when the medical image is displayed on a display device. A non-transitory computer-readable recording medium on which the program described in claim 12 is recorded.